Coordinated beamforming is a coordinated multiple points transmission mode, under which adjacent cells do not need to share data and mutually share interference channel information only through interfaces between base stations, each node only provides service for terminals in a coverage area and the influence of inter-cell interference is reduced through an interference coordination method. Coordinated beamforming is an important branch of a coordinated multiple points transmission technology and provides a compromise solution between backhaul overhead and system performance. Compared with joint processing, coordinated beamforming only needs to share channel state information between base stations, thus can coordinate and suppress inter-cell interference through methods such as transmission and receiving beam optimization, power control and user scheduling, etc, and can be easily implemented under the existing network architecture. As proved by researches, when the number of terminals in a system is enough, the system performance can be obviously improved through this interference coordination method.
Coordinated beamforming solutions mainly include a coordinated beamforming solution based on a duality theory and a coordinated beamforming solution based on a game theory. The former one mainly uses uplink and downlink duality theory to convert a transmission beamforming problem into a receiving beamforming problem to solve, so as to reduce implementation difficulty and computation complexity; and the latter one considers from an angle of games between cells, achieves system performance gains through different game rules, and the latter one is specifically divided into a non-cooperative egoistic solution, a cooperation-based altruistic solution and an egoistic and altruistic compromise solution.
In addition to the two major coordinated beamforming solutions, another novel solution is a coordinated beamforming solution based on Interference Alignment (IA). A basic principle of interference alignment is to design transmission precoding matrixes at a base station end to enable all interference signals to be superposed in one receiving signal subspace with dimensions which are as small as possible after signals are transmitted to terminals through wireless channels, enable desired signals to be in one subspace which is linearly independent of the subspace of the interference signals, and then interference is subjected to zero forcing at the terminals by using interference suppression matrixes, improving system capacity.
Interference alignment technology is a research hotspot in recent years and can fully use Degree of Freedom of a system to coordinate interference between terminals under the situation of greater interference. Through a precoding technology, interference alignment enables interference to be overlapped together at receiving ends, such that the influence of interference on desired signals is thoroughly eliminated. Different from the existing interference processing methods such as interference ignoring, interference decoding/elimination and orthogonal access (interference avoidance), etc, IA maximizes desired signal dimensions, i.e., DOF by reducing signal dimensions occupied by interference, and then interference is eliminated and desired signals are extracted by adopting methods, such as ZF (Zero Forcing) detection, etc, at terminals.
At present, there are mainly two means to obtain precoding matrixes (vectors) in the IA technology, a direct method and an iterative method. The direct method can obtain a closed-form solution of precoding matrixes and is relatively simple, but ideal global Channel State Information (CSI) needs to be known; and the iterative method uses reciprocity of uplink and downlink channels and optimizes a target function through alternate iteration in receiving and transmitting ends to obtain precoding matrixes, and implementation complexity is higher. The most representative methods include a distributed IA iterative algorithm put forward by Gomada, Jafar, et al., and a Maximum Signal Interference Noise Ratio (Max-SINR), herein the purpose of distributed IA is to minimize energy leaked by interference in a desired subspace, and the purpose of Max-SINR is to maximize receiving SINR. In many current researches, optimization and improvement are made based on the two methods. For example, in the direct method, precoding vector selection is performed on a classic IA solution based on a chordal distance criterion or an optimal characteristic sub-channel; and in the iteration-based distributed IA solution, the target function is gradually converted from minimization of power leaked by interference in the desired subspace to minimization of weighted summation of power leaked by minimized interference in a desired space and power leaked by the desired signal in an interference space.
The traditional interference alignment method is to align all interferences to one subspace with dimensions which are as small as possible. However, with the increase of the number of terminals, constraint conditions of interference alignment will sharply increase to cause alignment to be difficult to implement. Most partial interference alignment methods in the related art give fixed alignment modes and rarely there is a method of considering pertinent selection from many alignment modes. In addition, when a terminal uses a limited number of bits to feed CSI information back to a base station, due to quantization of channels or precoding, a system inevitably has a remarkable performance loss. This problem is particularly outstanding for interference alignment. Under the situation of limited feedback, full alignment of interference cannot be realized.
Moreover, a partial interference alignment solution aiming at more than two paths of interference signals (classic interference alignment only aims at the situation of two paths of interference) is also a hotspot of current researches on IA technology. To select which interferences to perform alignment becomes an important content of researches on partial interference alignment. However, current related researches are still comparatively few.